About
Protein phase separation has emerged as an important mechanism for
cellular compartmentalization, notably through liquid-liquid phase
separation (LLPS).
LLPS is a key mechanism for cellular compartmentalization, forming
membrane-less organelles that support critical physiological processes
like nuclear transcription and synaptic transmission. Dysregulation of
LLPS is linked to diseases such as amyotrophic lateral sclerosis (ALS),
underscoring the need to accurately identify phase-separating proteins
(PSPs).
To support this, over 20 computational tools have been developed to
predict LLPS behavior, using approaches ranging from simpler heuristic
rules to machine learning models.
However, these tools vary widely in their design and intended use,
and there is currently no unified resource to help researchers compare
and select the most appropriate one. This creates a barrier for
experimental scientists seeking efficient and accurate predictions.
Our work addresses this gap by providing a comprehensive review of
available LLPS predictors. We aim to offer a practical, centralized
guide to help researchers choose the right tool for their specific
scientific goals, ultimately reducing the cost and time of
trial-and-error experimentation.
Authors
Carlos Pintado-Grima
Carlos Pintado-Grima is a PhD student in Bioinformatics at the
Institute of Biotechnology and Biomedicine at the Autonomous University
of Barcelona (UAB). He obtained his degree in Biology and the Bachelor
of Science at UAB and Thompson Rivers University (Kamloops, BC, Canada).
He recieved his M.Sc. in Bioinformatics in 2020 at UAB. His current
research is focused on the development and analysis of bioinformatics
tools to better understand protein aggregation, folding and
misfolding.
Contact: Carlos.Pintado@uab.cat
Twitter: https://twitter.com/cpintadogrima
Oriol Bárcenas
Oriol Bárcenas is a PhD student in Bioinformatics, affiliated with
the Autonomous University of Barcelona (UAB) and the Spanish National
Research Council (CSIC). He completed his B.Sc degree in Biotechnology
at UAB in 2022, followed by an M.Sc. in Modelling for Science and
Engineering in 2023, also from UAB. His current research is focused on
the analysis of protein folding and aggregation data, alongside in
silico protein design and molecular dynamics (MD).
Contact: Oriol.Barcenas@uab.cat
Twitter: https://twitter.com/oriolbarcenas
